SSF technology has been known for centuries; at more or less 2600 BC, the Egyptianshave used it for making bread. Cassava bagasse is a starch-rich lignocellulosic residuewith 60-70% of residual starch, which was not extract at the industrial process of thecassava roots. Thousands of this residue is disposed daily at the environment. This solidraw residue can be used as the sole carbon source to produce fumaric acid by fermentationusing Rhizopus arrhizus at the process called solid-state fermentation. This production islargely confirmed to

the organisms of the order Mucorales, mainly to the genus Rhizopus.Cassava bagasse was milled, added nitrogen source and other salts. Temperature, pH,humidity and time of fermentation, were optimized using the neural network tool. Theproposal of this project was to optimize and evaluate the production of fumaric acid,through neural net work, an important tool to optimize and simulate processes.

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INTRODUCTION

Solid State Fermentation (SSF) may be compared to a tri phase system, solid, gas andliquid. Inside the fermentation media, water may be founded in three distinct ways, thewater linked (composition water), water weakly linked (solvatation water), and wateradsorbed (free water), but in SSF, free water is not found.

The grown support and thewater that carry the nutrients for the growth of themicroorganism constitute the solid phase. The carbon source may be the own support, forexample starch, or not when glucose is adsorbed in polyurethane, an inert support.

Cassava is a root (Mannihot esculenta Crantz) used as a meal or processed to obtaincassava flour. Cassava Bagasse is a solid waste composed by the fibrous material of roots,containing part of the starch not extracted in the process. Fumaric acid is an organic acid,used industrially

as an intermediate in chemical synthesis at sterifications reactions. It isused as food additive, anti-oxidant, acidulante in food industries, pH corrector; used inpharmaceutical industries to prepare medicines due to its properties of low toxicity andlowwater absorption.

Neural Network is a class of computers programs based in the working of the brains ofsuperior mammalians, in an attempt to imitate the intelligence (YAMAMOTO, 1998). Theutilization of Neural Network like a tool of optimizing process is crescent since 1943 whenMcCullough & Pitts first discuss the software imitation of biologic systems for data andinformation processing (BEZDEK, 1993). After the input of some data, the result of theproblem is given in the form of a response of the variable dependent (target variable) in theconditions proposed, different of the conditions input to the program. It is based on the factthat each independent variable affects the value of the response variable (dependent ortarget variable). The program simulates the real process and projects the value of thedependent variable at those particular conditions, based on the input data. Normally a groupof conditions with the dependent variables are tested, and the bigger value of the variableresponse is found if this response is given by one of the conditions tested. Theadvantageous of this program is that it is not necessary a regular interval between thevalues of the dependent variables.

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MATERIAL AND METHODS

Substrate

In this work it is utilized cassava bagasse given from Agroindustrial de Polvilho Ltda–

Paranavaí-

Paraná.

Cassava bagasse is milled to mesh 0.84 at 2.00 mm and utilized as thesole carbon source by the microorganism.

Saline Solution Used in the Medium Optimized 1

The saline

solution used to complement the nutritional needs of the microorganism ispresented at Table 1.

Table 1. Nutrients utilized to compose the saline solution used at the optimizedfermentation medium.

Nutrient

Amount

CaCO3

5 g

Biotin 0.0002% Solution

1.25

mL

ZnSO4.7H2O

0.01 g

MgSO4

0.0625 g

KH2PO4

0.0375 g

KNO3

3.76 g

Water

Up to: 250 mL

Fermentations

To optimize the fermentation physic conditions, the fermentation was carried onerlenmeyers flasks of 250 mL, with 5 g of the dry substrate (cassava bagasse),complemented with the saline solution shown at table 1 (1.5 mL/5 g substrate)

Fermentation Kinetic

The fermentation was carried on columns (Raimbault Columns), with samples beingcollected each day, in duplicate. (Two columns each day). Besides, the gas from thecolumns are analyzed in a Gas chromatography, on order to evaluate the content of CO2

(related to the microorganism growth), N2

and O2. This is a test of respirometry (data notshown).

Biomass Analysis

The protein formation (growth) was measured by the Kjeldahl method

(ADOLFO-LUTZ,1985).

Fumaric Acid

Fumaric Acid produced was measured by HPLC, High Performance LiquidChromatography.

Neural Network

It was made on a feedforward back-propagation neural network system that determinesthe best fermentation conditions for theTemperature, inoculation rate, pH, humidity, andfermentation time (days of fermentation). These are called independent variables, whichvariation affects the value of the dependent or response variable, thefumaric acid producedin each block of dependent variable.

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RESULTS AND DISCUSSION

The best fumaric acid producer is the strainRhizopus arrhizus

NRRL 2582 among theseveral strains ofRhizopus

studied (screening data not shown). With theRhizopus arrhizusNRRL 2582 strain, it was performed the optimization of the physical fermentationconditions, using the Neural Network tool.

The data obtained from the Neural Network Program Simulation is presented at Table 2. Itshows the response in fumaric acid to each block of tested conditions. Each block of theseconditions are the hypothesis to be tested at the program simulation.

C and best fermentationtime (13 days) as shown in table 2. It is very clear the importance of the humidity.Decreasing 0.5% the initial humidity, the fumaric acid produced decreased very hard. Thenetwork accuracy may be viewed in the Figures 1 and 2.

The curves are calculated on synaptic weights to each point determined by the network.

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Figure 2. Test-experiment values, circles are experimental values and lines are thecurve traced for the network.

The first graphic (Figure 1) is the relationship between the fumaric acid production in thetraining experiment

(real values fed to the program, and these data are used by the networkas a reference) and the objective value determined by the network. The second graphic(Figure 2) is the relationship between values of fumaric acid production in the testexperiments(real values fed to the program, but they are used as a check up for valuesvalidation) and the objective value determined by the network. In other words, in thesegraphics, the points assigned with circles are the experimental values determined forfumaric acid production in each group of variable (experimental conditions tested namedtraining experiments), when computer learns attributing different synaptic weights for eachvariable, and named experiments of test when the computer plots the error curve forpersonal verification. As near as the curve is to the experimental points, the most accurateis the analysis showed at Figure 2. According to Figure 2, the analysis done has a kind ofimprecision, but is satisfactory for our process.

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Figure 3. SSF kinetic for the production of FumaricAcid andBiomass.

According to the Neural Network, the best fermentation time is 13 days. Even being thebest time of fermentation determined in 13 days by the Neural Networks, a simple kinetic(Figure 3.) determinethat with 8 day of fermentation, the large amount of fumaric acid isstill formed. So fermentation with 8 days is economically the best time viable in an industryin some practical fermentations processes. Greater fermentation times will not furnish acorresponding increment at the fumaric acid production.

These experiments demonstrated that the Neural Networks really is useful tool todetermine the optimized fermentation conditions, and offers a way to verify theorically thefumaric acid produced in any conditions desired, but it has to be used very carefully,because an pure analysis without examine the process particularities may incur in error.

Process Scale-up

When one made a scale up of a fermentation system from 5g of dry cassava bagasse to atray system, through the column system it is critical the control of humidity. In columnsystem and in the tray system, the humidity must be 70 % at maximum, because 72.5 % istoo much water that drains from the support. This can cause eluation of the nutrientsinoculum and the risk of contamination at the bottom of the column and the trays by thewater that impregnate the cotton barrier to microorganisms that supports the weight of thecassava bagasse structure in the columns. In tray system humidity may evaporate very fastbecause of the large area and any kind of way must be used to keep the humidity, such assterilized water must be spilled to the system every day or twice per day, or theenvironmental humidity must be assured with water surface, and even so the productionmay decrease for the lost of water.

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CONCLUSION

The use of cassava bagasse showed to be a suitable substrate for fermentative processes,such as the production of fumaric acid in solid state fermentation (SSF), with molds of thegenusRhizopus,

when complemented with some salts and a nitrogen source. The importantstep of optimization of the fermentation conditions can be done using the Neural NetworkSystem, taking care with the peculiarities of the process. When working with the processscale up, the humidity is an important factor that must be kept in order to keep the moldactivity.